pynapple.process.decoding#
Decoding functions for timestamps data (spike times). The first argument is always a tuning curves object.
Functions
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Perform Bayesian decoding over a one dimensional feature. |
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Performs Bayesian decoding over 2 dimensional features. |
- pynapple.process.decoding.decode_1d(tuning_curves, group, ep, bin_size, time_units='s', feature=None)[source]#
Perform Bayesian decoding over a one dimensional feature. See: Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998). Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. Journal of neurophysiology, 79(2), 1017-1044.
- Parameters:
tuning_curves (pandas.DataFrame) – Each column is the tuning curve of one neuron relative to the feature. Index should be the center of the bin.
group (TsGroup, TsdFrame or dict of Ts/Tsd object.) – A group of neurons with the same index as tuning curves column names. You may also pass a TsdFrame with smoothed rates (recommended).
ep (IntervalSet) – The epoch on which decoding is computed
bin_size (float) – Bin size. Default is second. Use the parameter time_units to change it.
time_units (str, optional) – Time unit of the bin size (‘s’ [default], ‘ms’, ‘us’).
feature (Tsd, optional) – The 1d feature used to compute the tuning curves. Used to correct for occupancy. If feature is not passed, the occupancy is uniform.
- Returns:
Tsd – The decoded feature
TsdFrame – The probability distribution of the decoded feature for each time bin
- Raises:
RuntimeError – If group is not a dict of Ts/Tsd or TsGroup. If different size of neurons for tuning_curves and group. If indexes don’t match between tuning_curves and group.
- pynapple.process.decoding.decode_2d(tuning_curves, group, ep, bin_size, xy, time_units='s', features=None)[source]#
Performs Bayesian decoding over 2 dimensional features.
See: Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998). Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. Journal of neurophysiology, 79(2), 1017-1044.
- Parameters:
tuning_curves (dict) – Dictionary of 2d tuning curves (one for each neuron).
group (TsGroup, TsdFrame or dict of Ts/Tsd object.) – A group of neurons with the same keys as tuning_curves dictionary. You may also pass a TsdFrame with smoothed rates (recommended).
ep (IntervalSet) – The epoch on which decoding is computed
bin_size (float) – Bin size. Default is second. Use the parameter time_units to change it.
xy (tuple) – A tuple of bin positions for the tuning curves i.e. xy=(x,y)
time_units (str, optional) – Time unit of the bin size (‘s’ [default], ‘ms’, ‘us’).
features (TsdFrame) – The 2 columns features used to compute the tuning curves. Used to correct for occupancy. If feature is not passed, the occupancy is uniform.
- Returns:
Tsd – The decoded feature in 2d
numpy.ndarray – The probability distribution of the decoded trajectory for each time bin
- Raises:
RuntimeError – If group is not a dict of Ts/Tsd or TsGroup. If different size of neurons for tuning_curves and group. If indexes don’t match between tuning_curves and group.